Rank-Reduction Autoencoder (RRAE): A Breakthrough Nonlinear Model-Order Reduction Framework for Next-Generation Structural Damage Detection
Sebastian Rodriguez, B. Ferrándiz, Marc R'ebillat, N. Mechbal, A. Ammar, F. Chinesta
Structural Health Monitoring (SHM) aims to monitor in real-time the health state of engineering structures. For thin structures, Lamb Waves (LW) are particularly effective for SHM applications. A bonded piezoelectric transducer (PZT) generates LW in the form of a short tone burst, creating an initial wave packet (IWP) that propagates through the structure while interacting with boundaries, defects, and other features. These interactions leave precise yet complex signatures in the signals recorded by the sensors. In practice, however, extracting the specific signature associated with damage is often challenging for traditional SHM signal-processing methods, making reliable damage detection difficult. To address these limitations, we employ a Deep Learning–based approach known as the Rank Reduction Autoencoder (RRAE). The RRAE is an autoencoder whose latent space is constrained to follow a low-rank SVD structure, ensuring that it captures only the most salient features of the measured signals. In this work, we extend the original concept by enforcing the SVD modes to be predictive of both damage location and severity. This is achieved through an additional neural network that takes the reduced latent representation produced by the RRAE and directly estimates the damage characteristics. The joint training of the RRAE and the feature-extraction MLP therefore yields, at convergence, a robust and powerful tool for damage detection. The proposed technique is demonstrated on two case studies. The first, a more academic example, involves damage detection on a thin plate. The second is based on an experimental campaign conducted by CETIM and focuses on a 2-meter pipe segment, where the proposed architecture performs damage detection using measurements of acoustic wave emissions.